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THE ERΒ AGONIST, WT-IV-012, SUPPRESSES THE INFLAMMATORY RESPONSE IN SYSTEMIC LUPUS ERYTHEMATOSUS

2025· article· en· W4410715605 on OpenAlexvenueno aff
Shane Bruckner, B. Zeno, Chad Bennett, Wael Jarjour

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAgonistSystemic diseaseLupus erythematosusImmunologyInternal medicineImmunopathologyReceptorAntibody

Abstract

fetched live from OpenAlex

PV007 / #645 Poster Topic: AS02 - Animal Models Background/Purpose Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that causes inflammation in many of the body’s tissues, including the skin, joints, lungs, kidneys and heart. This inflammation causes damage to these tissues and produces significant mortality, with most complications in the early stages of the disease involving direct effects on various organs. SLE affects young women between the ages of 15 and 45 years at a 9:1 rate as compared to men. The etiology is complex and remains elusive, however the susceptibility of women during the years in which estrogen levels are at their highest may suggest a significant and critical contribution to the development of SLE. Estrogens are known to have pleiotropic effects on the immune system which is mediated through either estrogen receptor α (ERα), estrogen receptor β (ERβ) or the cell surface G-protein coupled receptor GPER1. ERα has been studied extensively in this context, however ERβ and GPER1 have received much less attention. Previously, our group and others have shown that ERα carrying immune cells are mediators of proinflammatory effects of estrogen more so than cells that lack this receptor. In the pursuit of a drug that is tailored to have a favorable selective estrogenic effect, the OSU Drug Development Institute discovered a novel carborane-based selective estrogen receptor modulator (SERM), WT-IV-012. This ERβ agonist exhibits potent binding of human ERβ (Ki = 2.0 nM) and functional selectivity for ERβ over ERα of at least 200-fold. The work presented herein describes the potential utility of WT-IV-012 in treating SLE in a humanized mouse model of the disease. Methods PBMC isolated from patients with active SLE were adoptively transferred into NSG mice and allowed to expand in vivo for 1 week. The mice were divided into 3 cohorts receiving either vehicle control, prednisone or WT-IV-012 via oral gavage on a daily basis for 5 weeks. Blood samples were taken at baseline, 3 and 5 weeks. Serum was analyzed for circulating cytokines using the MSD human V-PLEX Proinflammatory Panel. At 5 weeks, mice were euthanized, kidneys and hearts were harvested and processed for H&E and IHC histology and urine was collected and tested for proteinuria. Lupus patient PBMCs were isolated and stimulated under various conditions and then flow cytometry was used to identify specific cell types affected by WT-IV-012 and cytokine ELISAs were used to evaluate the cell culture supernatants. Results WT-IV-012 was as effective as prednisone in suppressing immune cell invasion of the kidney as well as inflammation of the heart. Furthermore, the ERβ agonist demonstrated superior effectiveness in suppressing proinflammatory cytokines as compared to prednisone as well as reducing the proteinuria seen in the vehicle-treated control mice. The in vitro effect of WT-IV-012 confirmed the suppression of IFNγ and TNFα and revealed the cell type specific effects of the drug. Conclusions WT-IV-012 is an effective inhibitor of the SLE inflammatory process and warrants additional study as a potential therapeutic in patients with SLE.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.291
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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